Derived metrics for the game of Go – intrinsic network strength assessment and cheat-detection

  title={Derived metrics for the game of Go – intrinsic network strength assessment and cheat-detection},
  author={A. Egri-Nagy and Antti T{\"o}rm{\"a}nen},
  journal={2020 Eighth International Symposium on Computing and Networking (CANDAR)},
The widespread availability of superhuman AI engines is changing how we play the ancient game of Go. The open-source software packages developed after the AlphaGo series shifted focus from producing strong playing entities to providing tools for analyzing games. Here we describe two ways of how the innovations of the second generation engines (e.g. score estimates, variable komi) can be used for defining new metrics that help deepen our understanding of the game. First, we study how much… Expand
1 Citations
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  • Zero paper.,
  • 2019
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  • R. Hickey
  • Computer Science
  • Proc. ACM Program. Lang.
  • 2020
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  • 2019
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  • 2019